


'''simple docstring'''


import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
      import jax

      from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel







class 							__magic_name__	(       unittest.TestCase ):

     """simple docstring"""






     def __init__(   self			,     lowerCamelCase			,     lowerCamelCase=13			,     lowerCamelCase=30			,     lowerCamelCase=2			,     lowerCamelCase=3			,     lowerCamelCase=True			,     lowerCamelCase=True			,     lowerCamelCase=32			,     lowerCamelCase=5			,     lowerCamelCase=4			,     lowerCamelCase=37			,     lowerCamelCase="gelu"			,     lowerCamelCase=0.1			,     lowerCamelCase=0.1			,     lowerCamelCase=10			,     lowerCamelCase=0.02			,     ):


          '''simple docstring'''





          __A	:       Any      =							parent
          __A	:       Dict      =							batch_size
          __A	:       Any      =							image_size
          __A	:       Union[str, Any]      =							patch_size
          __A	:       Dict      =							num_channels
          __A	:       Dict      =							is_training
          __A	:       List[str]      =							use_labels
          __A	:       List[str]      =							hidden_size
          __A	:       List[str]      =							num_hidden_layers
          __A	:       Optional[int]      =							num_attention_heads
          __A	:       Dict      =							intermediate_size
          __A	:       int      =							hidden_act
          __A	:       int      =							hidden_dropout_prob
          __A	:       List[Any]      =							attention_probs_dropout_prob
          __A	:       Any      =							type_sequence_label_size
          __A	:       Any      =							initializer_range

          # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
          __A	:       List[str]      =							(image_size // patch_size) ** 2
          __A	:       Optional[int]      =							num_patches + 1


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Dict      =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

          __A	:       str      =							ViTConfig(
              image_size=self.image_size			,     patch_size=self.patch_size			,     num_channels=self.num_channels			,     hidden_size=self.hidden_size			,     num_hidden_layers=self.num_hidden_layers			,     num_attention_heads=self.num_attention_heads			,     intermediate_size=self.intermediate_size			,     hidden_act=self.hidden_act			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     is_decoder=__snake_case			,     initializer_range=self.initializer_range			,     )

          return config, pixel_values


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							FlaxViTModel(config=__snake_case		)
          __A	:       Optional[int]      =							model(__snake_case		)
          # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
          __A	:       Dict      =							(self.image_size, self.image_size)
          __A	:       Optional[int]      =							(self.patch_size, self.patch_size)
          __A	:       List[str]      =							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
          self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, num_patches + 1, self.hidden_size)		)


     def lowerCAmelCase__  (   self			,     lowerCamelCase			,     lowerCamelCase		):


          '''simple docstring'''





          __A	:       List[str]      =							self.type_sequence_label_size
          __A	:       Optional[int]      =							FlaxViTForImageClassification(config=__snake_case		)
          __A	:       Any      =							model(__snake_case		)
          self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)		)

          # test greyscale images
          __A	:       Optional[Any]      =							1
          __A	:       List[str]      =							FlaxViTForImageClassification(__snake_case		)

          __A	:       List[str]      =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
          __A	:       str      =							model(__snake_case		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Optional[Any]      =							self.prepare_config_and_inputs()
          (
              (
              __A    
          )  ,(
              __A    
          )  ,    
          )	:       Tuple      =							config_and_inputs
          __A	:       List[Any]      =							{"pixel_values": pixel_values}
          return config, inputs_dict







@require_flax
class 							__magic_name__	(       snake_case__    ,		unittest.TestCase ):

     """simple docstring"""






     lowerCamelCase__       						=				(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       str      =							FlaxViTModelTester(self		)
          __A	:       Union[str, Any]      =							ConfigTester(self			,     config_class=__snake_case			,     has_text_modality=__snake_case			,     hidden_size=37		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          self.config_tester.run_common_tests()


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Tuple      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__snake_case		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A	:       Union[str, Any]      =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*__snake_case		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       Optional[int]      =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A	:       List[Any]      =							model_class(__snake_case		)
               __A	:       Tuple      =							inspect.signature(model.__call__		)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               __A	:       int      =							[*signature.parameters.keys()]

               __A	:       Any      =							["pixel_values"]
               self.assertListEqual(arg_names[:1]			,     __snake_case		)


     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          __A  ,__A	:       List[Any]      =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__		):
                    __A	:       Any      =							self._prepare_for_class(__snake_case			,     __snake_case		)
                    __A	:       Dict      =							model_class(__snake_case		)

                    @jax.jit
                    def model_jitted(lowerCamelCase			,     **lowerCamelCase		):
                         return model(pixel_values=__snake_case			,     **__snake_case		)

                    with self.subTest("JIT Enabled"		):
                         __A	:       Optional[Any]      =							model_jitted(**__snake_case		).to_tuple()

                    with self.subTest("JIT Disabled"		):
                         with jax.disable_jit():
                              __A	:       Dict      =							model_jitted(**__snake_case		).to_tuple()

                    self.assertEqual(len(__snake_case		)			,     len(__snake_case		)		)
                    for jitted_output, output in zip(__snake_case			,     __snake_case		):
                         self.assertEqual(jitted_output.shape			,     output.shape		)


     @slow
     def lowerCAmelCase__  (   self		):


          '''simple docstring'''





          for model_class_name in self.all_model_classes:
               __A	:       Dict      =							model_class_name.from_pretrained("google/vit-base-patch16-224"		)
               __A	:       Union[str, Any]      =							model(np.ones((1, 3, 224, 224)		)		)
               self.assertIsNotNone(__snake_case		)





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




